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Setting up a Text Summarization Project
When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested... Read more
What’s the Main Priority for Data Labeling in Modern ML, Quality or Scale: Experts Weigh In
AI relies on data labeling for training algorithms – without the data, there can’t be any machine learning. Data labeling accuracy is often difficult to achieve on its own, but it becomes even more of an issue when scalability is at stake. It’s believed by many... Read more
5 Tools for Getting Started with Data Science on GitHub
Depending on who you ask, the definition of “data scientist” can vary from “Excel expert” to “deep learning engineer” to “MLOps practitioner” – working individually, or as part of a team. Given this broad spectrum of software engineering experience, it can be challenging for data scientists... Read more
What Is the Difference Between Test Data and Live Data?
Data has become a hot topic of discussion, but many of these conversations oversimplify what it covers. Those outside of data science operations refer to data as a singular resource or group when there are actually many different forms of data that serve various purposes. One... Read more
7 Most Common Big Data Blunders Every Business Should Avoid
As more companies invest in big data and analytics, there is growing confusion around the best data practices and how businesses should leverage their new investment. While it is essential for businesses to understand how to maximize big data’s potential, it’s equally as important to know... Read more
Is Groovy a Viable Language for Data Science Applications? 5 Pros and Cons
Choosing the right programming language can make a remarkable difference in data science applications. While the industry standards are Python and R, some data scientists have branched off to use others they prefer. One such possible alternative is the Groovy programming language. Apache Groovy is an... Read more
6 Most Common Errors When Implementing AI and Machine Learning
Artificial intelligence and machine learning are steadily rising in popularity, but how can organizations and businesses avoid errors when implementing AI? New technology can be challenging to navigate at first, especially for organizations that aren’t digitally dextrous to begin with. Artificial intelligence (AI) and its partner... Read more
Git vs SVN: What’s the Difference?
Managing the source code is one of the key factors in any development environment. Version control systems or VCS came into prominence to offer an effective solution to the code management needs while facilitating a version-controlled multi-user environment. With the growing popularity of practices like Infrastructure... Read more
Computer Vision & Data Annotation – An Easy Way of Understanding the Relevance in the Real World
Here, you will find a brief explanation of computer vision, some cases we are experiencing in real life, and some of the existent techniques in data annotation supporting the advance of computer vision. I want to highlight upfront that I’m not approaching any computer vision algorithms... Read more
Seven Questions to Ask Before Implementing AI in Your Enterprise
Artificial intelligence is the talk of the digital town, and probably will be for many more years to come. Due to the surge in AI’s popularity across several industries, many businesses are eagerly investing in this technology. While artificial intelligence is undoubtedly transforming the way we... Read more